Methods for predicting the optimum and the range of soil water contents for tillage based on the water retention curve

Information is needed on the range of soil water contents for tillage. The objective of the work was to develop methods for the prediction of the soil water contents at which tillage may be done satisfactorily. Three water contents are considered: the lower (dry) limit, the optimum water content, and the upper (wet) limit. This paper makes a synthesis of published results from tillage and soil physics experiments and also includes some new experimental results. The effects of tillage are considered in relation to some “fixed points” including the lower plastic limit, field capacity and a new fixed point “the inflection point”. These considerations lead to methods for prediction of the lower (dry) tillage limit, the optimum water content, and the upper (wet) tillage limit in terms of the parameters of the van Genuchten equation for soil water retention. Predictions can be made in terms of soil composition through the use of pedotransfer functions for the parameters of the van Genuchten equation. The new methods will enable the effects of soil degradation and climate change on tillage work days to be estimated. The results are potentially mappable using geographic information systems.     

Estimating the wetting branch of the soil water retention curve from grain-size fractions

The development of a theoretical method for estimating the wetting branch of the soil water retention curve (SWRC) is required for knowledge of the soil infiltration process. The aim of this study was to derive the theoretical functions to represent the wetting branch of the SWRC based on the Jensen method, and then compare the Jensen method and Kool & Parker (KP87) model for estimating the wetting branch of the SWRC. Fifteen soil samples with varying basic properties (e.g., grain-size distribution and bulk density (BD)) were selected from the Unsaturated Soil Hydraulic Database (UNSODA) to test these two methods. Results showed that the Jensen method (root mean squared error (RMSE) = 0.057 cm³ cm⁻³) produced a substantially better performance in predicting the wetting branch of the SWRC than the KP87 (RMSE = 0.089 cm³ cm⁻³) for the 15 samples. The range of the scaled mean bias error (SMBE) between the Jensen method-predicted and measured soil water contents at all pressure heads was −0.529 to 0.402. A positive linear relationship between the SMBE and silt content was observed for the Jensen method. The findings of this study have practical significance for simulating the soil infiltration in the unsaturated zone.     

根据土壤粒径分形估计紫色土水分特征曲线

选择不同质地紫色土,分别根据土壤颗粒的数量和重量分布计算土壤粒径分形维数,并与Tyler和Wheatcraft土壤水分特征曲线模型的拟合分形维数进行比较分析。结果表明,三种途径获得的分形维数与土壤质地密切相关,均表现出土壤黏粒含量越高,质地越细,分形维数越高。土壤粒径数量分布分形维数(2.98～3.26)和土壤粒径重量分布分形维数(2.73～2.81)存在较大的差异,但二者间却存在显著的线性关系。土壤粒径分形维数与土壤水分特征曲线模型拟合分形维数(2.72～2.84)均存在显著的线性关系,尤其是土壤粒径重量分布分形维数与土壤水分特征曲线模型拟合分形维数数值十分接近。通过建立的土壤水分特征曲线模型分形维数与土壤粒径分布分形维数关系式,结合Tyler和Wheatcraft模型进行土壤水分特征曲线预测,预测值与实测值具有良好的一致性,因而根据土壤粒径分形预测紫色土水分特征曲线是可行的。     

估计太湖地区水稻土水分特征曲线的物理-经验方法研究

为寻求适于太湖地区水稻土水分特征曲线估算的简便方法,本文以实测土壤基本性质和土壤水分特征曲线为基础,对13种物理-经验方法估计土壤水分特征曲线的效果进行综合评价。结果表明,基于形状相似性的物理-经验方法预测效果要优于多数分形方法,但均差于基于Kravchenko-Zhang分维方法和B rooks-Corey水分特征曲线方程的孔隙表面分形模型(简称KZBC模型)。物理-经验方法在不同压力水头段对含水量的预测精度有差异,在实际工作中不仅要考虑模型的适用性,还要考虑其适用的压力水头范围。KZBC模型估计土壤水分特征曲线精度最高,实用性也较强,是适用于本研究区水分特征曲线估计的最佳方法。     

Pedotransfer functions for estimating soil water retention curve of Sicilian soils

Pedotransfer functions (PTFs) make use of routinely surveyed soil data to estimate soil properties but their application to soils different from those used for their development can yield inaccurate estimates. This investigation aimed at evaluating the water retention prediction accuracy of eight existing PTFs using a database of 217 Sicilian soils exploring 11 USDA textural classes. PTFs performance was assessed by root mean square differences (RMSD) and average differences (AD) between estimated and measured data. Extended Nonlinear Regression (ENR) technique was adopted to recalibrate or develop four new PTFs and Wind’s evaporation method was applied to validate the effectiveness of the relationships proposed. PTFs evaluation resulted in RMSD and AD values in the range 0.0630–0.0972 and 0.0021–0.0618 cm³ cm^–3, respectively. Best and worst performances were obtained respectively by PTF-MI and PTF-ZW. ENR allowed to recalibrate PTF-MI and PTF-ZW with improvements of RMSD (0.0594 and 0.0508 cm³ cm^–3) and to develop two relationships that improved RMSD by 75–78% as compared to PTF-MI. The results confirmed the potential of ENR technique in calibrating existing PTFs or developing new ones. Validation conducted with an independent dataset suggested that recalibrated/developed PTFs represent a viable alternative for water retention estimation of Sicilian soils.     

Transferability of continuous‐ and class‐pedotransfer functions to predict water retention properties of semiarid Syrian soils

Hydraulic properties of soils, particularly water retention, are key for appropriate management of semiarid soils. Very few pedotransfer functions (PTF s) have been developed to predict these properties for soils of Mediterranean regions, where data are particularly scarce. We investigated the transferability of PTF s to semiarid soils. The quality of the prediction was compared to that for soils originating from temperate regions for which most PTF s were developed. We used two soil data sets: one from the Paris basin (French data set, n  = 30) and a Syrian data set (n  = 30). Soil samples were collected in winter when the water content was near field capacity. Composition and water content of the samples were determined at seven water potentials. Continuous‐ and class‐PTF s developed using different predictors were tested using the two data sets and their performance compared to those developed using artificial neural networks (ANN ). The best performance and transferability of the PTF s for both data sets used soil water content at field capacity as predictor after stratification by texture. The quality of prediction was similar to that for ANN ‐PTF s. Continuous‐ and class‐PTF s may be transferable to other countries with performances that vary according to their ability to account for variation in soil composition and structure. Taking into account predictors of composition (particle size distribution, texture, organic carbon content) and structure (bulk density, porosity, field capacity) did not lead to a better performance or the best transferability potential.     

砒砂岩风化物对土壤水分特征曲线及蒸发的影响

运用高速离心机法和室内模拟蒸发的方法,对比研究了红色(RS)、灰色(GS)和白色(WS)三种颜色砒砂岩风化物在不同添加比例条件下对沙黄土和风沙土水分特征曲线、供水能力和干旱过程中失水过程的影响。研究表明:砒砂岩风化物的添加对沙黄土和风沙土水分特征曲线有较明显的影响。砒砂岩风化物的持水能力均高于沙黄土和风沙土,砒砂岩风化物添加提高了风沙土和沙黄土的持水能力,降低了沙黄土和风沙土失水速率;较低比例的砒砂岩风化物提高了沙黄土和风沙土的比水容量,但对风沙土和沙黄土低吸力段供水能力的提高作用并不明显。整个蒸发期间,砒砂岩风化物降低了沙黄土的标准蒸发量,而对于风沙土,其作用是蒸发前期降低,中后期增加。红色、灰色、白色三种砒砂岩风化物分别使风沙土的蒸发失水比提高了11.59%、10.14%和0.01%,添加三种砒砂岩风化物后的处理的最终平均含水量分别是风沙土的4倍、2.33倍、1.33倍,使沙黄土的蒸发失水比降低了13.33%、13.33%、29.52%,最终含水量降低了45.83%、54.17%、66.67%。因此,从提高土壤持水能力方面考虑,砒砂岩可以用于当地矿区和排土场区土壤改良,以促进生态修复。     

基于土壤质地数据估算土壤保水性能Van Genuchten参数

The van Genuchten (vG) function is often used to describe the soil water retention curve (SWRC) of unsaturated soils and fractured rock. The objective of this study was to develop a method to determine the vG model parameter m from the fractal dimension. We compared two approaches previously proposed by van Genuchten and Lenhard et al. for estimating m from the pore size distribution index of the Brooks and Corey (BC) model. In both approaches we used a relationship between the pore size distribution index of the BC model and the fractal dimension of the SWRC. A dataset containing 75 samples from the UNSODA unsaturated soil hydraulic database was used to evaluate the two approaches. The statistical parameters showed that the approach by Lenhard et al. provided better estimates of the parameter m. Another dataset containing 72 samples from the literature was used to validate Lenhard’s approach in which the SWRC fractal dimension was estimated from the clay content. The estimated SWRC of the second dataset was compared with those obtained with the Rosetta model using sand, silt, and clay contents. Root mean square error values of the proposed fractal approach and Rosetta were 0.081 and 0.136, respectively, indicating that the proposed fractal approach performed better than the Rosetta model.     

秸秆不同还田方式对土壤低吸力段持水能力及蒸发特性的影响

利用张力计测定蒸发过程中低吸力段土壤水分特征曲线,对比研究了秸秆在经过粉碎、氨化及与无机土壤改良剂混合三种措施处理后对土壤饱和含水量、低吸力段土壤持水及供水能力和早期干旱过程中土壤脱水速度的影响。结果显示:粉碎秸秆施入土壤后提高了土壤饱和含水量、持水及供水能力,而长秸秆对土壤饱和含水量、持水及供水能力的影响较小;粉碎并氨化的秸秆对土壤饱和含水量、持水及供水能力的提高作用更为显著。粉碎秸秆及长秸秆处理均易造成早期干旱过程中脱水速度过快,氨化后或当秸秆与无机土壤改良剂混合施入土壤时,土壤低吸力段脱水速度明显减慢,利于土壤有效水的保存。该结果为提出新的能最大效率发挥秸秆改良土壤作用的秸秆还田方式提供了一定的理论基础。     

不同盆栽基质水分特征曲线的对比分析与模拟

现有关于盆栽控水模拟土壤干旱条件的试验中多采用含水率作为水分胁迫阈值,然而由于基质配比不同导致含水率相同的基质的水分状况也不尽相同,这导致各研究间结果难以对比和参考。为快速获取盆栽基质水分特征曲线,建立基质水分特征曲线预测模型。该研究以盆栽控水试验常用的泥炭土、蛭石和珍珠岩为基质材料,测定了不同配比基质的水分特征曲线,通过不同方法（多元回归模型、人工神经网络）建立了其预测模型。结果表明,人工神经网络模型对泥炭土-蛭石复配基质水分特征曲线的预测精度高于多元回归;相较于人工神经网络,多元回归模型的稳定性更高。综合考虑模型的精度和稳定性,多元回归模型是预测作物盆栽基质水分特征曲线的最佳模型,预测精度R2≥0.950,平均误差接近0。该模型为基质水分特征曲线快速获取以及相关作物干旱胁迫研究间的对比提供了方法和依据。     

Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity

Modeling water flow and solute transport in vadose zone requires knowledge of soil hydraulic properties, which are water retention and hydraulic conductivity curves. As an alternative to direct measurement, indirect determination of these functions from basic soil properties using pedotransfer functions (PTFs) has attracted the attention of researchers in a variety of fields such as soil scientists, hydrologists, and agricultural and environmental engineers. In this study, PTFs for point and parametric (van Genuchten's parameters) estimation of soil hydraulic parameters from basic soil properties such as particle-size distribution, bulk density, and three different pore sizes were developed and validated using artificial neural network (ANN) and multiple-linear regression methods and the predictive capabilities of the two methods was compared using some evaluation criteria. Total of 195 soil samples was divided into two groups as 130 for the development and 65 for the validation of PTFs. Although the differences between the two methods were not statistically significant (p > 0.05), regression predicted point and parametric variables of soil hydraulic parameters better than ANN. Both methods had lower accuracy in parametric predictions than in point predictions. Accuracy of the predictions was evaluated by the coefficient of determination (R²) and the root mean square error (RMSE) between the measured and predicted parameter values. The R² and RMSE varied from 0.637 to 0.979 and from 0.013 to 0.938 for regression, and varied from 0.444 to 0.952 and from 0.020 to 3.511 for ANN, respectively. Even though regression performs insignificantly better than ANN in this case, ANN produces promising results and its advantages can be utilized by developing or using new algorithms in future studies.     

Comparison of seven water retention functions used for modelling soil hydraulic conductivity due to film flow

The unsaturated soil hydraulic conductivity accounting for film flow is important for understanding soil hydrological and biological processes, especially in arid and semi‐arid regions. Recently, a theoretically based hydraulic conductivity model was developed to describe the hydraulic conductivity as a function of water content. We have used this model to compare seven soil water retention functions commonly used for predicting soil hydraulic conductivity due to film flow. A total of 30 soils, varying in basic properties, were selected from the Unsaturated Soil Hydraulic Database to evaluate the seven functions. The Webb method was applied to identify the critical soil matric potential (h _c) below which thin film flow controls water movement. Soil hydraulic conductivity measurements at matric potential below h _c were then used for curve fitting according to the seven functions. Slight differences were observed among the functions in predicting soil hydraulic conductivity due to film flow. Six of the seven functions in combination with the hydraulic conductivity model described the hydraulic conductivity due to film flow well, according to the terms of the coefficient of efficiency. The relatively poor performance of the one exception was due to the fact that the linear shape of the function made it less flexible at low matric potentials. In addition, the effect of textural class on its performance was substantial, showing a poorer fit for the sand soil compared with the loam and clay soils. These findings have important applications related to soil and water resources conservation especially in arid and semi‐arid regions.     

Water retention and hydraulic conductivity of a loamy sand soil as influenced by crop rotation and fertilization

Measurements are reported of soil organic carbon content, dry bulk density, water retention characteristics, and saturated hydraulic conductivity of a sandy loam soil with two different crop rotations and two levels of fertilization. The water retention characteristics were fitted to the van Genuchten equation. Values of unsaturated hydraulic conductivity were estimated by calculation. It was found that crop rotation has much larger effects on these soil physical properties than fertilization. Water retention and hydraulic conductivity are greater when mustard, and clover with grass are included in the crop rotation, but only at water contents greater than 0,10 and 0, 13 kg kg^?1respectívely.     

Estimating water retention curves of forest soils from soil texture and bulk density

Forest soils differ significantly from the arable land in their distribution of the soil bulk density and humus content, but the water retention parameters are primarily derived from the data of agricultural soils. Thus, there is a need to relate physical parameters of forest soils with their water retention characteristics and compare them with those of agricultural soils. Using 1850 water retention curves from forest soils, we related the following soil physical parameters to soil texture, bulk density, and C content: air capacity (AC), available water capacity (AWC), and the permanent wilting point (PWP). The ACs of forest soils were significantly higher than those of agricultural soils which were related to the low bulk densities of the forest soils, whereas differences in AWCs were small. Therefore, for a proper evaluation of the water retention curves (WRCs) and the parameters derived from them, further subdivisions of the lowest (< 1.45 g cm^‐3) of the three bulk density classes was undertaken to the wide range of low soil densities in forest soils (giving a total of 5 bulk density classes). In Germany, 31 soil texture classes are used for the estimation of soil physical parameters. Such a detailed classification is not required because of insignificant differences in WRCs for a large number of these classes. Based on cluster analysis of AC, AWC, and PWP parameters, 10 texture collectives were obtained. Using 5 classes of bulk densities, we further calculated the ACs, AWCs, and the PWPs for these 10 classes. Furthermore, “van Genuchten parameters” (θ r, θ s, α, and n) were derived which described the average WRC for each designated class. In a second approach using multiple regression analysis, regression functions for AC, AWC, and PWP and for the van Genuchten parameter were calculated.     

鲁中南山地典型植被土壤颗粒与土壤水分特征曲线的分形学特征

土壤粒径分布与水分特征曲线是土壤的重要物理性质,对土壤侵蚀状况和土壤肥力有显著影响。以鲁中南山地典型植被下土壤为研究对象,运用分形学理论研究5种典型植被土壤颗粒与水分特征曲线的分形学特征。结果表明:1)不同植被类型的土壤颗粒单重分形维数、多重分形参数和土壤水分特征曲线分形维数具有显著差异,均表现为麻栎+刺槐混交林>黑松+黄连木混交林>黑松林>核桃林>荒草地;2)土壤分形维数有林地大于荒草地,混交林大于纯林;3)土壤分形维数与黏粒体积分数、粉粒体积分数呈显著正相关,与砂粒体积分数呈显著负相关;4)土壤颗粒单重分形维数与土壤水分特征曲线分形维数呈显著的正相关关系。说明土壤颗粒分布与土壤水分特征曲线的分形参数可以作为反映土壤结构性状变化的定量指标,可利用土壤颗粒分形与土壤水分特征曲线分形维数的相关关系来描述对应的土壤水分特征曲线。研究成果可为鲁中南山地退耕还林与生态造林工程建设及其生态效益评价提供理论参考。     

青海三江源地区土壤水分常数转换函数的建立与比较

利用土壤理化性质数据建立转换函数是间接获得土壤水力参数的重要手段之一。基于测定的土壤理化性质和土壤水分常数数据,本文采用回归分析、BP神经网络和基于BP神经网络的Rosetta模型3种方式分别建立了青海三江源地区土壤饱和含水量、毛管持水量和田间持水量的转换函数,并对其预测精度进行了比较。结果表明:(1)回归分析方法总体预测效果比较理想,特别是田间持水量的平均误差(ME)和均方根误差(RMSE)都在3.397%以下,决定系数(R2)高达0.868;(2)BP神经网络方法的预测效果非常理想,各土壤水分常数平均误差和均方根误差都在4.685%以下,并且决定系数均在0.857以上;(3)Rosetta模型的预测效果相对较差,特别是饱和含水量和毛管持水量,平均误差(ME)和均方根误差(RMSE)相对较大,决定系数(R2)相对较小。3种方式中,BP神经网络方法所建立的毛管持水量和饱和含水量转换函数均为最佳,回归方法所建立的田间持水量的转换函数要好于BP神经网络方法和Rosetta模型,Rosetta模型对土壤水分常数的预测效果不如其他两种方式。研究可为青海三江源地区土壤水力特性参数研究以及区域尺度上土壤水分估算提供科学依据。     

Assessment of the usefulness of particle size distribution measured by laser diffraction for soil water retention modelling

Particle size distribution (PSD) is a major soil characteristic, which is essential and commonly used for the development of pedotransfer functions (PTFs) to estimate the water retention of soils. The laser diffraction method (LDM) became a popular alternative to the standard sieve‐hydrometer method (SHM) of PSD measurement. Unfortunately, PSDs determined with LDM and SHM methods differ sometimes substantially. Moreover, it is claimed that the laser diffraction method underestimates finer fractions in favor of coarser fractions. Several authors have tried to elaborate on methods to recalculate LDM PSD into its SHM counterparts, but no universal methodology has been developed to this date. In this paper, we use PSD determined by LDM directly for PTF development and compare it with the classical PTF approach based on PSD measured by SHM. Four different PTF models based on LDM particle size distribution data were developed, with different PSD characteristics taken as the models' input variables. The possibility of using alternative PSD characteristics, such as deciles, area moment mean and volume moment mean, for PTF development was examined. The accuracy of PTF models constructed on the basis of LDM‐measured PSD was comparable with that of the developed models using texture data obtained from SHM, giving approximately the same RMSE and R² values. Our study shows that LDM‐measured particle size distribution may be directly used for PTF developments without any recalculations to their sieve‐hydrometer counterparts.